This paper aims to investigate the impact of macroeconomic
variables on economic growth after Structural Adjustment Programme (SAP)
in Pakistan. In doing so, study utilises the quarterly time series data
from 1991Q1 to 2007Q4. Advanced Autoregressive Distributed Lag Model
(ARDL) approach has been employed for co-integration and error
correction model (ECM) for short-run results in the case of Pakistan.
Empirical investigations indicate that credit to private sector
(financial development), foreign direct investment and inflow of
remittances correlate positively with economic growth in the long run.
High inflation rate and trade-openness slow down the speed of growth
rate in short as well as long run.

JEL classification: O1, C22

Keywords: Growth, ARDL Cointegration

INTRODUCTION

Economically developed countries have been able to reduce their
poverty level, strengthen their social and political institutions,
improve their quality of life, preserve natural environments and achieve
political stability [Barro (1996); Easterly (1999); Dollar and Kraay
(2002a); Fajnzylber, Lederman, et al. (2002)]. After the World War II,
most of the countries adopted aggressive economic policies to improve
the growth rate of real gross domestic product (GDP). The neoclassical
growth models imply that during the evolution between steady states;
technology, exogenous rate of savings, population growth and technical
progress generate higher growth levels [Solow (1956)].

Endogenous growth model developed by Romer (1986) and Lucas (1988)
argue that permanent increase in growth rate depends on the assumption
of constant and increasing returns to capital. (1) Similarly, Barro and
Lee (1994) investigate the empirical association between human capital
and economic growth. They seem to support endogenous growth model by
Romer (1990) that highlight the role of human capital in economic
activity. Fischer (1993) argues that long-term growth is negatively
linked with inflation and positively correlated with better fiscal
performance and factual foreign exchange markets. In the context of
developing countries, investment both in capital and human capital,
labour force, ability to adapt technological changes, open trade polices
and low inflation are necessary for economic growth.

Since 1988, Pakistan's economic management, have been almost
totally dependent on Structural Adjustment Programme (SAP). Focus of the
SAP is on improving the balance of payments, cutting the fiscal deficit,
lowering inflation and improving economic growth rate. This programme
has focused on improving the balance of payments through devaluation of
local currency, cutting down the fiscal deficit, decreasing government
size and liberalising trade. Beneficiaries of economic reforms are
consume by poor governance, lack of transparency in economic policies,
high level of corruption, high burden of internal and external debts and
interest rate payments on these debts, weak sitttation of law and order,
and improper implementation of economic policies. Singer (1995) argues
that the SAP are based on the assumption that the first and most
necessary step is to get the macroeconomic fundamentals right. Supply
will respond to the fight environment and proper price enticement and
this leads to sustainable growth. This seems to neglect some of the SAP
impediment to domestic supply. Furthermore, a small developing open
economy has limited international capital mobility or financial
integration; higher domestic saving results in higher investment and
economic growth under the assumption of "investment and domestic
savings are highly correlated" (Fledstien-Horioka Hypothesis).

A Brief Look on Relevant Literature

Barro (1996) seems to document that high inflation in a country
reduces the rate of economic growth. Many studies find no strong
positive association between openness and growth of the economy. Grilli
and Milesi-Ferretti (1995) do not support the hypothesis that inflow of
foreign capital promotes growth. Rodrik (1998) shows no significant
correlation between financial liberalisation and growth in small open
economies. Similarly, Edison (2003) does not find strong evidence of a
relationship between trade liberalisation and growth. He also concludes
that financial integration does not promote the growth per se, without
controlling for some economic, financial, institutional and policy
characteristics. Edwin and Shajehan (2001) support that apart from
growth in the labour force, investment in skill and technology, as well
as low inflation rate and open trade polices, are important for economic
growth. Moreover, the ability to adopt beneficial technological shocks
in order to increase efficiency is also necessary.

Since many developing countries have a large agricultural sector,
adverse supply shocks in this sector are likely to originate an adverse
impact on economic growth. Growth in agriculture has a positive impact
on industrial and service sector's growth, social infrastructure is
an important determinant of the investment decisions [Krishna (2004)].
The author however stresses that there is a need for exploring other
approaches to explain economic growth from all perspectives. Recent
empirical studies confirm that natural resources, climate, topography
and 'land lockedness' have a direct impact on economic growth
affecting agricultural productivity, economic structure, transportation
costs and competitiveness in goods markets [Sachs and Warner (1997),
Bloom and Sachs (1998); Masters and McMillan (2001); Armstrong and Read
(2004)]. However, others [e.g. Rodrik, et al. (2002); Easterly and
Levine (2003)] find no effect of geography on growth after controlling
for institutions. Edwin and Shajehan (2001) empirically suggest that
apart from growth in the labour force, investment in both physical and
human capital, as well as low inflation and trade liberalisation polices
are essential for economic growth. They also suggest the ability to
adopt technological changes in order to increase efficiency is also
important.

Klein and Olivei (2003) utilises quadratic interaction between
income per capita and capital inflow or financial liberalisation and2
established a positive and significant effect of capital account
openness along with stock market liberalisation on economic growth for
middle-income countries but not for poor and rich countries. In small,
open economies, absorption capacity for capital is limited because the
financial markets are impulsive. The excessive capital inflows towards
small open economies might cause "Dutch" disease phenomena and
asymmetric information might be inefficient use of capital [Carlos, et
al. (2001); Hauskrecht, et al. (2005)].

Stark and Lucas (1988); Taylor (1992); and Faini (2002) establish
the positive relationship between remittances and economic growth.
Empirical evidence of previous studies of the impact of worker's
remittances on economic growth as well as poverty reduction is mixed
[Juthathip (2007)]. The results suggests that, remittances have a
significant impact on poverty reduction in developing economies through
increasing income tends to relax the consumption constraints of the
poor, they have a nominal impact on growth working through enhance in
both domestic investment and human capital development. On the basis of
recent and quite literal evidence, surveyed by Lopez and Olmedo (2005)
analyse the positive impact of remittances on education and
entrepreneurship at the household-level. The mechanism through which
remittances can positively affect growth can be better results in
micro-econometric studies based on household-level data. (3) Chaudhary,
et al. (2002) investigate the role of trade instability on investment
and economic growth. The results show that export instability does not
affect economic growth and investment in Pakistan. However export
instability could affect foreign exchange earnings and as a result it
could have negative impact on imports and economic growth. Chaudhary, et
al. (2007) examine the impact of trade policy on economic growth in
Bangladesh. Results strongly support a long-run positive and significant
relationship among exports, imports and economic output for Bangladesh.
Furthermore, empirical evidence shows relationships between exports and
output growth and also between imports and output growth in the
short-run. A strong feedback effect between import growth and export
growth has also been established.

A number of studies have examined determinants of economic growth
in case of Pakistan in terms of a mixture of factors that includes
income, real interest rate, dependency ratios, foreign capital inflows,
foreign aid, changes in terms of trade, and openness of the economy such
as [Iqbal (1993, 1994), Khilji and Mahmood (1997); and Shabbir and
Mahmood (1992)]. Iqbal (1994) seems to investigate the relationship
between structural adjustment lending and real output growth. The
empirical results indicate negative link between structural adjustment
lending and output growth, and worsening the terms of trade and economic
output in the country. Finally, favourable weather condition and real
domestic savings stimulate the real economic growth rate. Furthermore,
Iqbal (1995) examines a three-gap model and concludes that real
devaluation, increased foreign demand and capacity utilisation are main
contributors of economic growth in the country. Khilji and Mahmood
(1997) seem to document that military expenditures are contractionary to
economic growth in the case of Pakistan. Shabbir and Mahmood (1992)
posit positive impact on real GNP growth of foreign private investment.
The empirical results by Iqbal and Zahid (1998) reveal that openness of
trade is positively associated with economic growth while budget deficit
and external debt reduce growth of output. Iqbal and Satar, (2005) come
to conclusion that foreign workers' remittances impact economic
growth positively with high significance. Public and private investment
is also an important source of economic growth in the country. But
inflation rate, external debt and worsening situation of terms of trade
are appeared to be correlated with economic growth negatively. Finally,
Shahbaz (2009) reassesses the impact of some macroeconomic variables on
economic growth. The results reveal that financial sector's
development improves the performance of the economy in the long run.
Credit to private sector as a share of GDP, used as a proxy for
financial development, is a good predictor of economic growth for case
of Pakistan. Similarly, rise in exports and investment boost economic
growth while Inflation and imports both reduce economic growth. High
economic growth is found to be associated with small size of the
government.

To better understand the growth process, this study develops an
empirical model using a time series approach for the country specific
case of Pakistan. This attempts to explore the some of the necessary
factors for sustained economic growth in the country. The rest
organisation of the papers is as follows; Section II explains the model
and data collection procedure, Section III describes methodological
framework and Section IV investigating the empirical results. Finally,
Section V presents conclusion and policy recommendations.

II. MODEL AND DATA

International Financial Statistics (IFS) (2008) and Economic Survey
of Pakistan (various issues) have been combed to obtain the data of said
variables. Finally, quarterly data for GDP per capita has been collected
from Ahmed (2007). (4) The study utilises the data period from 1991Q1 up
to 2007Q4. Log-linear model has been constructed to find the required
linkages. It provides better results than simple linear regression.
Above discussed literature permits us to construct empirical model as
following:

Financial sector's development stimulates the economic growth.
Financial development improves productivity of investment projects and
lowers the transaction cost that increases more investment activities.
Development of financial also increases savings in the country and
savings in resulting contributes to economic growth positively [Pagano
(1993)].

Financial openness (foreign direct investment) promotes economic
growth though improved technology transferee, efficiency, improvement in
the quality of production factors and enhanced production. This will not
only lead to increase in exports but also increases in savings rate in
the country. The increased savings rate definitely stimulates investment
opportunities that ultimately faster growth of output and employment
[Khor (2000)].

It is expected that increased foreign remittances raises the
economic growth. Continuous flow of remittances is important sources to
lower current account and external borrowing as well. Remittances play
its role to decline external debt and maintain exchange rate of an
economy. External shocks are absorbed through sustainable flow of
foreign remittances. So impact of remittances on economic growth may be
positive because foreign remittances are perquisite to accelerate real
output in the country [Iqbal and Satar (2005)].

International trade posits that economic growth can be accelerated
through openness of an economy through its effects of increased
competition, easy access to trade opportunities on efficiency of
resource allocation. These positive externalities such as access to
advanced technology with its spillovers effects, and availability of
necessary inputs from rest of the world increase domestic output and
hence economic growth in the country.

In the literature, there are two arguments about link between
inflation and economic growth. In first view, Mundell (1963) and Tobin
(1965) document that high inflation increases the cost of holding money.
It leads to increase the shift 6f capital from money portfolio that
improves investment and hence economic growth. But rise in inflation
retards economic growth through various channels. For instance, cost of
capital that is increased due to high inflation lessens investment rate
and rate of capital accumulation which declines real growth rate. High
inflation rate encourages inflation tax to rise and alleviates the
incentive to work. Thus rise in unemployment will not only reduce real
output but also decline economic growth.

III. METHODOLOGICAL FRAMEWORK

In recent times, Ng-Perron (2001) developed four test statistics
utilising GLS detrended data [D.sup.d.sub.t] . The calculated values of
these tests based on the forms of Philip-Perron (1989) [Z.sub.[alpha]]
and [Z.sub.t] statistics, the Bhargava (1986) [R.sub.1] statistics, and
the Elliot, Rotherberg and Stock (1996) created optimal best statistics.
The terms are defined as follows:

k = [t.summation over (t=2)] ([D.sup.d.sub.t-1]).sup.2]/[T.sup.2]
(2)

While de-trended GLS tailored statistics are given below:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (3)

If [x.sub.t] = {1} in fist case and [x.sub.t] = {1,t} in second.
(5)

In economic literature, many methods are bluntly used for
conducting the cointegration test; the most widely used methods include
the residual based Engle-Granger (1987) test, and Maximum Likelihood
based Johansen (1991) and Johansen-Juselius (1990) tests. All these
require that the variables in the system be of equal order of
integration. The residual-based co-integration tests are inefficient and
can lead to contradictory results, especially when there are more than
two I(1) variables under consideration.

Recently, an emerging body of literature led by Pesaran and Shin
(1995), Pesaran, Shin and Smith (1996), Pesaran and Shin (1997), and
Pesaran, Shin and Smith (2001) has introduced an alternative
co-integration technique known as the "Autoregressive Distributive
Lag" or ARDL bounds testing. It is argued that ARDL has a numerous
advantages over conventional techniques like Engle-Granger and Johansen
Cointegration approaches. The first advantage of ARDL is that it can be
applied irrespective of whether underlying regressors are purely I(0),
purely I(1) or mutually co-integrated [Pesaran and Pesaran (1997)]. The
second advantage of using the bounds testing approach to co-integration
is that it performs better than Engle and Granger (1987), Johansen
(1990) and Philips and Hansen (1990) co-integration tests in small
samples [see for more details Haug (2002)]. The third advantage of this
approach is that, the model takes sufficient number of lags to capture
the data generating process in a general-to-specific modeling framework
[Laurenceson and Chai (2003)]. Finally, ARDL is also having the
information about the structural break in time series data. However,
Pesaran and Shin (1995) contented that, appropriate modification of the
orders of the ARDL model is sufficient to simultaneously correct for
residual serial correlation and the problem of endogenous variables.

Under certain environment, Pesaran and Shin (1995) and PSS (6)
[Pesaran, Shin, and Smith (2001)] established that long run association
among macroeconomic variables may be investigated by employing the
autoregressive distributive lag model. After the lag order for ARDL
procedure, OLS may be utilised for estimation and identification. Valid
estimations and inferences can be drawn through presence of unique long
run alliance. Such inferences not only on long run but also on short run
coefficients may be made which lead us to conclude that the ARDL model
is correctly augmented to account for contemporaneous correlations
between the stochastic terms of the data generating process (DGP), also
that ARDL estimation is possible even where explanatory variables are
endogenous. Moreover, ARDL remains valid irrespective of the order of
integration of the explanatory variables. But ARDL procedure will
collapse if any variable is integrated at I(2).

The PSS (2001) procedure is implemented to estimate error
correction model given such an equation:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (4)

PSS F-test is estimated by imposing zero-joint restriction on
[delta]'s in error correction model. Distribution of PSS F-test is
non-standard [Chandan (2002)]. The reason is that lower and upper
critical bounds are generated by PSS (1996). Lag order of ARDL model is
selected on lower value of AIC or SBC. After empirical estimation, if
PSS (2001) confirms the presence of unique cointegration vector among
variables. This shows that one is outcome variable while other is
forcing actor in model. On basis of selected ARDL, long run and short
estimates can be investigated in two steps [Pesaran and Shin (1995)].

Long run relationship for said actors can be established by
estimating ARDL model as given by means of Ordinary Least Squares (OLS):

Where v is normally distributed error term. Long run
(cointegration) coefficients can be obtained:

Y = [alpha] + [rho]X = [[mu].sub.t] (5)

From Equation 6:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (7)

Firstly, we try to find out the direction of relationship between
economic growth and its determinants in the case of Pakistan by
analysing the PSS F-test statistics. The calculated F-statistic is
compared with the critical value tabulated by Pesaran and Pesaran (1997)
or Pesaran, et al. (2001). (7)

The ARDL method estimates [(p+1).sup.k] number of regressions in
order to obtain optimal lag length for each variable, where p is the
maximum number of lags to be used and k is the number of variables in
the equation. The model can be selected using the model selection
criteria like Schwartz-Bayesian Criteria (SBC) (8) and Akaike's
Information Criteria (AIC). SBC is known as the parsimonious model:
selecting the smallest possible lag length, whereas AIC is known for
selecting the maximum relevant lag length. In the second step, the long
run relationship is estimated using the selected ARDL model. When there
is a long run relationship between variables, there should exist an
error correction representation.

Finally, the error correction model is estimated. The error
correction model results indicate the speed of adjustment back to the
long run equilibrium after a short run shock. To determine the integrity
of fit of the ARDL model, the diagnostic tests are conducted. The
diagnostic or sensitivity tests examine the serial correlation,
autoregressive conditional heteroscedisticity, normality of error term
and heteroscedisticity associated with the model.

IV. EMPIRICAL FINDINGS

ARDL has the advantage of avoiding the classification of variable
into I (0) or I(1) since there is no need for unit root pre-testing. As
argued by Sezgin and Yildirim, (2002) that ARDL can be applied
regardless of stationary properties of variables in the sample and
allows for inferences on long run estimates, which is not possible under
alternative co-integration techniques. In contrast, according to
Ouattara (2004) in the presence of I(2) variables the computed
F-statistics provided by PSS (2001) become invalid because bounds test
is based on the assumption that the variables should be I(0) or I(1).
Therefore, the implementation of unit root tests in the ARDL procedure
might still be necessary in order to ensure that none of the variable is
integrated of order I(2) or beyond.

For this purpose, Ng-Perron (2001) test is employed which is more
powerful and reliable for small data set. To find out the integrating
order, ADF [Dicky and Fuller (1979), P-P [Philip and Perron (1989)] and
DF-GLS [Elliot, et al. (1996)] tests are often used respectively (9).
Due to the poor size and power properties, both tests are not reliable
for small sample data set [Dejong, et al. (1992) and Harris (2003)].
They concluded that these tests seem to over-reject the null hypotheses
when it is true and accept it when it is false. Therefore, Ng-Perron
test utilised to overcome these above-mentioned problems about order of
integration of running actors. Results of unit root estimation reveal
that all variables are having unit root problem at their level form as
shown in Table 1.

Established order of integration leads us to apply the ARDL
approach to find out cointegration among macroeconomic variables. So lag
length for conditional error correction version of ARDL model has been
obtained by means of swartz bayesian criteria (SBC) and akaike
information criteria (AIC) through Vector auto regressive (VAR). With
such type of time series data set, we cannot take lag more than 4 lags
(see Table 2). The calculated F-Statistics is 5.674 that is higher than
upper bound 4.37 and lower bound 3.29 at 1 percent level of
significance. This implies that alternative hypothesis of cointegration
may be accepted. It is concluded that there prevails cointegration among
macroeconomic variables. After establishing cointegration among running
actors in model, we can employ ARDL regression to investigate the long
run elasticities.

Table 3 reveals the impact of independent variables on dependent
one. Improved performance of financial sector enhances the speed of
economic growth significantly. It is concluded that 9 percent
improvement in the efficiency of financial sector causes the economic
growth by 4.18 percent to rise. Continuous inflows of remittances effect
economic growth positively with minimal significance. Economic growth is
negatively caused by increased trade-openness significantly. This
reveals the low demand of country's exports in international market
due to low quality. Trade history of the country shows the high
dependence on imports as compare to exports which increases trade
deficit and hence slows down the speed of economic growth. Financial
openness correlates positively with economic activity in the country and
improves economic growth rate. A 10 percent increase in FDI inflows
(financial openness) will improve economic growth by 0.3 percent.
Inflationary situation retards the economic growth, 0.16 percent of
economic growth is eroded by 10 percent increase in inflation.

The [ecm.sub.t-1] coefficient indicates how quickly/slowly
variables return to equilibrium and it should have a negative sign with
high significance. The error correction term, [ecm.sub.t-1] shows the
speed of modification required to re-establish equilibrium in the
short-run model. Bannerjee, et al. (1998) argue that the error
correction term is significant at 5 percent level of significance. The
coefficient of [ecm.sub.t-1] is equal to -0.2705 for the short-run model
and implies that deviation from the long-term economic growth is
corrected by 27.05 percent over each year. The lag length of the
short-run model is selected on the basis of the schwartz bayesian
criteria.

In short span of time, economic growth is improved through previous
supporting policies. Development of financial sector declines economic
growth significantly as shown in Table 4. This shows that improvements
in financial sector could not stimulate the economic activity in short
span of time. Actually, financial activities take time to contribute in
economic activity through capital formation process. Financial openness
affects the economic growth positively but insignificant. Remittances
and inflation lower down economic growth insignificantly. Finally,
trade-openness and economic growth are inversely linked with
significance.

Sensitivity Analysis and Stability Tests

The results for serial correlation, autoregressive conditional
heteroskedasticity, normality and heteroskedasticity (sensitivity
analysis) are presented in Table 2. These results show that the
short-run model passed the diagnostic tests. The empirical estimations
indicate that there is no evidence of autocorrelation and that the model
passes the test for normality, the error term is also proved to be
normally distributed. There is no existence of white heteroscedasticity
in the model.

Finally, for analysing the stability of the long-run coefficients
together with the short-run dynamics, the cumulative sum (CUSUM) and the
cumulative sum of squares (CUSUMsq) are applied. According to Pesaran
and Shin (1999), the stability of the estimated coefficient of the error
correction model should also be empirically investigated. A graphical
representation of CUSUM and CUSUMsq is shown in Figures 1 and 2.
Following Bahmani-Oskooee and Nasir (2004) the null hypothesis (i.e.,
that the regression equation is correctly specified) cannot be rejected
if the plot of these statistics remains within the critical bounds of
the 5 percent significance level. As it is clear from Figures 1 and 2,
the plots of both the CUSUM and the CUSUMsq are within the boundaries,
and, hence these statistics confirm the stability of the long-run
coefficients of the regressors that affect the economic growth in the
country. The stability of the selected ARDL model specification was
evaluated using the CUSUM and the CUSUMsq of the recursive residual test
for structural stability [see Brown, Durbin and Evans (1975)]. The model
appears to be stable and correctly specified given that neither the
CUSUM nor the CUSUMsq test statistics exceed the bounds of the 5 percent
level of significance (see figure given in appendix).

V. CONCLUSIONS AND POLICY IMPLICATIONS

Over the last two decades the determinants of economic growth have
been the primary focus of theoretical and applied research. Generally,
it has been observed that both developing and developed countries with
strong macroeconomic fundamentals tend to grow faster than those without
them. Despite the lack of a unifying theory, there are several partial
theories that argue the role of various factors in determining the
economic growth.

This study explores some of the causal factors for sustained
economic growth in the country after the Structural Adjustment Programme
(SAP). This programme was initiated as part of a massive world-wide
policy measures under the directive of 1MF. It aimed to improve the
balance of payments through devaluation of local currency, cutting the
fiscal deficit and reducing subsidies, decreasing government size and
liberalising trade.

Empirical psychology reveals that ARDL bounds testing approach
employed to find out the cointegration among running macroeconomic
variables. ARDL F-statistic confirmed about the existence of long run
association. Financial sector's development seems to stimulate
economic activity and hence increases economic growth in long span of
time but in short run. Remittances are positively correlated with
economic growth in the country. Trade-openness erodes economic growth
while financial openness promotes it. Domestic investment activities
generate employment opportunities and in resulting contribute to improve
economic growth. Finally, increased inflation and economic growth
correlated inversely in the country.

The findings show that structural adjustment program adopted by
government was totally failed to fill its objectives. This study could
not incorporate other important macroeconomic variables for economic
growth due unavailability of data (quarterly). There is a need to make
comprehensive study to find out impact of other macroeconomic variables
in the country. Further research on this particular topic will provide
inclusive policy implications to enhance growth rate in the country.

Carlos, et al. (2001) When Does Capital Account Liberalisation Help
More Then Hurts? NBER Working Paper Series. (Working Paper 8414).

Chandana, Kularatne (2001) An Examination of the Impact of
Financial Deepening on Long-Run Economic Growth: An Application of a
VECM Structure to a Middle-Income Country Context. 2001 Annual Forum at
Misty Hills, University of the Witwatersrand, Johannesburg.

Harris, R. and R. Sollis (2003) Applied Time Series Modeling and
Forecasting. West Sussex: Wiley.

Hauskrecht, A. and Nhan Le (2005) Capital Account Liberalisation
for a Small Economy. Indiana University, Kelley School of Business,
Department of Business Economics and Public Policy in its series
(Working Papers 13.)

Juthathip, Jongwanich (2007) Workers' Remittances, Economic
Growth and Poverty in Developing Asia and the Pacific Countries.
Economic and Social Commission for Asia and the Pacific. (Working Paper
WP/07/01.)

Khilji, N. M. and A. Mahmood (1997) Military Expenditures and
Economic Growth in Pakistan. The Pakistan Development Review 36:4,
791-808.

Khor, M. (2000), Globalisation and the South. Some Critical Issues.
Third World Network, Penang.

Lopez, Cordova E. and A. Olmedo (2005a) International Remittances
and Development: Existing Evidence, Policies and Recommendations. Paper
prepared for the G-20 Workshop on "Demographic Challenges and
Migration" held in Sydney on 27-28 August 2005.

(1) Previous theories on growth were based on the assumption of
constant return to scale. But increasing productivity due to
improvements in human capital, technological developments, more
investment in research and development (R&D) violate this
assumption. This phenomenon has been stressed in various endogenous
growth models. The strong relation between innovation and economic
growth has also been empirically affirmed by many studies [see Fagerberg
(1987); Lichtenberg (1992); Ulku (2004)].

(2) The result is supporting with the view that poorer countries do
not have the legal, social, and political institution would necessary to
full enjoy the benefits of capital account liberalisation.

(3) Better understanding to see Lopez Cordova (2005) on education a
study for Mexico McCormick and Wahba (2001) on entrepreneurship in
Egypt; Dustmann and Kirchkamp (2002) on entrepreneurship in Turkey:
Nishat and Bilgrami (1991) also found that remittances have positive
impact on consumption, investment and imports. Similarly, Iqbal and
Sattar (2005) found that workers' remittances appeared to be the
third important source of capital for economic growth in Pakistan.

(7) If the F-test statistic exceeds the upper critical value, the
null hypothesis of no long-run relationship can be rejected regardless
of whether the underlying orders of integration of the variables are
I(0) or I(1). Similarly, if the F-test statistic falls below the lower
critical value, the null hypothesis is not rejected. However, if the
sample F-test statistic falls between these two bounds, the result is
inconclusive. When the order of integration of the variables is known
and all the variables are I(1), the decision is made based on the upper
bounds. Similarly, if all the variables are I(0), then the decision is
made based on the lower bounds.

(8) The mean prediction error of AIC based model is 0.0005 while
that of SBC based model is 0.0063 [Shrestha (2003)].

(9) We also utilised these three tests but decision in based on
Ng-Perron test.

Muhammad Shahbaz and Khalil Ahmad
are MPhil students and A. R. Chaudhary
is Professor of Economics at National
College of Business Administration and Economics, Lahore, Pakistan.